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Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
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Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection.

Sai Wang1, Hai-Wei Shen1, Hua Chai1

  • 1Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau.

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|February 15, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces CHR-DE, a novel penalized model for identifying high correlation genes in complex biological data. It effectively selects groups of genes for cancer and genetic disease research.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Biostatistics

Background:

  • Identifying high correlation genes from high-dimensional data is crucial for understanding cancer and genetic diseases.
  • Gene expression data presents challenges due to inherent correlation structures and gene groupings (e.g., by function, location).
  • Selecting relevant biomarkers from such complex datasets requires advanced computational methods.

Purpose of the Study:

  • To propose a novel penalized accelerated failure time model, CHR-DE, for effective group-based biomarker selection.
  • To leverage non-convex regularization (Complex Harmonic Regularization - CHR) and global optimization (Differential Evolution - DE) for enhanced feature selection.
  • To develop an efficient algorithm for optimizing the proposed penalized model.

Main Methods:

  • Development of the CHR-DE model integrating Complex Harmonic Regularization (CHR) with Differential Evolution (DE).
  • CHR approximates a combination of L1 and Lq (1 ≤ q < 2) norms for group-wise biomarker selection.
  • DE is employed for global hyperparameter optimization of CHR, within a wrapper-embedded memetic framework.
  • An efficient path seeking algorithm was developed for model optimization.

Main Results:

  • CHR-DE demonstrated a strong capability in selecting groups of genes from high-dimensional biological data.
  • The model was evaluated on synthetic data and three real-world gene expression datasets (breast cancer, hepatocellular carcinoma, colorectal cancer).
  • Experimental results confirmed CHR-DE's effectiveness compared to existing methods.

Conclusions:

  • CHR-DE is a powerful and effective tool for group-based feature selection in high-dimensional gene expression data.
  • The proposed method enhances biomarker discovery for cancer and genetic disease research.
  • CHR-DE offers improved prediction accuracy and biological interpretability.